Twitter Algorithm Can Detect Riots Faster Than Police, Says Study
LONDON: Twitter posts can help track riots and other violent events much before they are reported to the police, according to a study which shows that social media can be an invaluable source of information for law-enforcement officials.
An analysis of data taken from the London riots in 2011 showed that computer systems could automatically scan through Twitter and detect serious incidents, such as shops being broken into and cars being set alight, before they were reported to the UK Metropolitan Police Service.
The system, developed by researchers at Cardiff University in the UK, could also discern information about where the riots were rumoured to take place and where groups of youths were gathering.
The research, published in the journal ACM Transactions on Internet Technology, showed that on average the computer systems could pick up on disruptive events several minutes before officials and over an hour in some cases.
Researchers believe that their work could enable police officers to better manage and prepare for both large and small scale disruptive events.
"We have previously used machine-learning and natural language processing on Twitter data to better understand online deviance, such as the spread of antagonistic narratives and cyber hate," said Pete Burnap from Cardiff University.
"In this research we show that online social media are becoming the go-to place to report observations of everyday occurrences - including social disorder and terrestrial criminal activity," Burnap said.
"This research could augment existing intelligence gathering and draw on new technologies to support more established policing methods," he said.
Researchers analysed 1.6 million tweets relating to the 2011 riots in England, which began as an isolated incident in Tottenham on August 6 but quickly spread across London and to other cities in England, giving rise to looting, destruction of property and levels of violence not seen in England for more than 30 years.
They used a series of machine-learning algorithms to analyse each of the tweets from the dataset, taking into account a number of key features such as the time they were posted, the location where they were posted and the content of the tweet itself.
Results showed that the machine-learning algorithms were quicker than police sources in all but two of the disruptive events reported.
When the first reports of disorder occurring in Enfield were received by the police, the researchers showed that their system could have picked up this information from Twitter one hour and 23 minutes earlier.
Scientists are continually looking to the swathes of data produced from Twitter, Facebook and YouTube to help them to detect events in real-time.
Estimates put social media membership at about 2.5 billion non-unique users, and the data produced by these users have been used to predict elections, movie revenues and even the epicentre of earthquakes.